A Review Paper on Biomedical Data Analysis using Machine Learning

Biomedical data analysis, driven by machine learning, plays a pivotal role in healthcare and life sciences. Despite challenges like high dimensionality, noise, and class imbalance, data preprocessing ensures quality through cleaning, feature extraction, and normalization. Classification and regression models aid in tasks like disease diagnosis and drug development, while feature selection enhances accuracy and reduces overfitting. Dimensionality reduction techniques like PCA improve efficiency by grouping related data points and revealing disease subtypes. Deep learning is proving to be an effective tool in the biomedical industry since it can analyse text, pictures, and genetic data. Strict validation guarantees the generalizability of the model and openness in the decision-making procedures. Integrating domain knowledge is essential for tackling moral issues like data integrity and patient privacy. Applications of precision medicine in clinical contexts include therapy prescription, personalized medicine, and illness prediction. Biobanking is transformed by machine learning, which makes managing and analyzing big datasets easier. There are obstacles to real-world deployment, such as trust-building, data access, and regulatory compliance. Precision medicine has emerged, transforming healthcare procedures and propelling scientific study in the biomedical field.